Method of and system for conducting personalized federated search and presentation of results therefrom

ABSTRACT

The present disclosure provides user-interface methods and systems for submitting search requests to search engines and presenting search results therefrom customized using content preferences learned about a user, comprising sending query information to at least two search engines, including a query identifying desired content, and user information, including context information describing the environment in which the query information is being sent, and a user signature representing content preferences learned about the user; receiving at least one set of a search result and auxiliary information from the at least one search engine in response to sending the query information, including information describing attributes of the search result that led to the search result being chosen by the at least one search engine; ordering the at least one search result based at least in part on the auxiliary information; and presenting the ordered search results to the user.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit under 35 U.S.C. §119(e) ofProvisional Application Ser. No. 61/381,532, filed Sep. 10, 2010,entitled Method of and System for Conducting Personalized FederatedSearch and Presentation of Results Therefrom, the contents of which areincorporated by reference herein.

FIELD OF THE DISCLOSURE

The present disclosure relates generally to systems for searching usingmultiple search engines and, more specifically, to systems for federatedsearching and presentation of results therefrom.

BACKGROUND OF THE DISCLOSURE

There is a proliferation of personal devices such as smartphones,tablets and other devices with relatively large amounts of flash memorypaired with relatively small amounts of Random Access Memory (RAM). Suchdevices are expected to outstrip the growth of devices such as desktopand notebook personal computers. Personal devices with large amounts offlash memory are capable of locally storing large amounts of personaldata, including but not limited to contact information, email, messages,applications, settings and utilities, documents, music, videos, images,etc.

However, in some implementations, accessing these large amounts ofpersonal data using personal devices is significantly more tedious thanon desktop and notebook personal computers because personal devices havesmall or constrained form-factors, and virtual and/or smaller and/orambiguous keypads with which to input text. Inputting text to enterintent is almost always more tedious on personal devices than on desktopor notebook computers where user displays and input mechanisms are lessconstrained. However, systems enabling search and browse functionalitiesof the large amounts of content on personal devices are still based onthose search and browse systems originally developed for desktop andnotebook computers.

In view of the foregoing, in some embodiments, it is understood thatthere are significant problems and shortcomings with federated searchingon personal devices based in part on the difficulty of text input andconstrained screen displays, coupled with small amounts of RAM, largeamounts of flash memory, and large amounts of personal data.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of various embodiments of the presentinvention, reference is now made to the following descriptions taken inconnection with the accompanying drawings in which:

FIG. 1 illustrates a system for personalized federated search wheresearch is done on a network system and results are returned to a clientdevice in accordance with some embodiments;

FIG. 2 illustrates a system for personalized federated search wheresearch is done on a network system and results are returned to a clientdevice in accordance with some embodiments;

FIG. 3 illustrates a diagram that depicts the various components of anetwork used in a system for personalized federated search in accordancewith some embodiments;

FIG. 4 illustrates a screenshot of an example search response for auser's search query in a system for personalized federated search inaccordance with some embodiments;

FIG. 5 illustrates a screenshot of an example search response for a userhaving a different user signature in a system for personalized federatedsearch in accordance with some embodiments;

FIG. 6 illustrates a screenshot of an example search response for auser's incremental search query in a system for personalized federatedsearch in accordance with some embodiments;

FIG. 7 illustrates a screenshot of an example search response for auser's incremental search having a different user signature in a systemfor personalized federated search in accordance with some embodiments;and

FIG. 8 illustrates a diagram that depicts the various components of auser device, in accordance with some embodiments.

SUMMARY OF THE DISCLOSURE

The present disclosure provides user-interface methods and systems forsubmitting search requests to at least two search engines and presentingsearch results therefrom customized using content preferences learnedabout a user, the method comprising sending query information to the atleast two search engines, the query information including (a) a queryidentifying desired content, and (b) user information, the userinformation including (i) context information describing the environmentin which the query information is being sent, the context informationadapted into a syntax understandable by at least one of the at least twosearch engines, and (ii) a user signature representing contentpreferences learned about the user; receiving at least one set of asearch result and auxiliary information from the at least one of the atleast two search engines in response to sending the query information,the auxiliary information of the set including information describingattributes of the search result of the set that led to the search resultbeing chosen by the at least one of the at least two search engines;ordering the at least one search result based at least in part on theauxiliary information; and presenting the ordered search results to theuser.

Under another aspect of the invention, the auxiliary informationincludes at least one of: an identification of a dataspace associatedwith the corresponding search result, an identification of a searchengine that provided the search result, a relevance list, wherein arelevance list element represents a set of an attribute of the searchresult and a value indicating the relevance of the attribute to at leastone of the query and the user information, a textual location list,wherein a textual location list element represents the location ofsearch terms from the query information in fields provided by the searchresult, a disjunction indicator list, wherein a disjunction indicatorlist element represents whether a corresponding search term from thequery information matched the search result, a spelling correction list,wherein a spelling correction list element represents whether acorresponding search term from the query information exactly matched asubstring of fields from the search result, and wherein the spellingcorrection list element represents a value indicating the distancebetween the corresponding search term and the matched substring, if amatch was based on an applied spelling correction, a complete word matchlist, wherein a complete word match list element represents whether acorresponding search term from the query information exactly matched afull word of fields from the search result based on an applied spellingcorrection, a field match list, wherein a field match list elementrepresents whether the search query matched all words of a field of thesearch result, a location proximity indicator representing a value forthe distance from the search result to a specified location, and alocation query-place indicator representing whether a part of the searchquery matched an entity recognized to be a place having a location.

Under another aspect of the invention, the ordering is further based onsearch engine general information, the search engine general informationdescribing the at least one search engine.

Under another aspect of the invention, the ordering based on the searchengine general information is based on a comparison between the searchengine general information and the user information.

Under another aspect of the invention, the search engine generalinformation includes at least one of a measure of importance relative toother search engines of reordering criteria of different metacontentdescriptor fields, a measure of importance relative to other searchengines of spelling correction matches, a measure of importance relativeto other search engines of disjunction indicators, relativeinterpretation of distance compared to other search engines for locationindicators, whether the mere existence of a search result from the atleast one search engine indicates that the search result should bepresented as important, and the relative importance compared to othersearch engines of complete word matches.

Under another aspect of the invention, the method further comprisesselecting the search engines to which the query information is sentbased on a measure of the use by the user of the corresponding searchengines.

Under another aspect of the invention, the query identifying desiredcontent includes at least one of a search query and a browse query,wherein the search query is initiated via text input by the user, andwherein the browse query is initiated via the user performing an actionin a device application other than text input.

Under another aspect of the invention, the content preferences learnedabout the user include at least one of a user profile, user behavior,and user activity.

DETAILED DESCRIPTION OF EMBODIMENTS

The present disclosure describes a system of and method for federatedsearch that enables universal personalized incremental search andrecommendations, designed for personal devices, to improve contentsearch and access to search results and personal content. Federatedsearch refers to an information retrieval technology that allows forsubstantially simultaneous search of multiple searchable resources,referred to as search engines. A device makes a single query which isdistributed to search engines participating in the federation. A modulethen aggregates the search results that are received from the searchengines for presentation to the user.

Incremental search refers to search input technology in which inputsearch words are incomplete or ambiguous. An example incomplete input isseen where a search input allows the user to input only a prefix of adesired search word. Techniques for selecting a set of resultsresponsive to the user's partial search query include, but are notlimited to, those disclosed in U.S. Pat. No. 7,895,218, entitled Methodand System For Performing Searches For Television Content Using ReducedText Input, filed May 24, 2005, and U.S. patent application Ser. No.11/246,432, entitled Method and System For Incremental Search WithReduced Text Entry Where The Relevance of Results is a DynamicallyComputed Function of User Input Search String Character Count, filedOct. 7, 2005, all of which are incorporated by reference herein.However, an incremental search method is not required, as the interfacecan be used with queries comprising full complete terms, which aredispatched with an explicit send action.

In some embodiments, the search system accepts ambiguous input. Anexample ambiguous input is seen where a search input is accepted fromnon-QWERTY keypads such as overloaded number pads seen on cellulartelephones or television remote controls. In such embodiments, eachnumber on such a number pad, such as 2, refers ambiguously to input ofmultiple letters, such as A, B, or C. Techniques for selecting a set ofresults responsive to the user's ambiguous search query include, but arenot limited to, those disclosed in U.S. Pat. No. 7,788,266, entitledMethod and System For Processing Ambiguous, Multi-Term Search Queries,filed Sep. 27, 2005, which is incorporated by reference herein.Furthermore, an incremental search system optionally returns resultswithout the user hitting a return or enter button. Accordingly, searchresults in such a system arrive and are presented as the user isentering input. Advantageously, one result of allowing incrementalsearch on input- or display-constrained devices is to significantlyreduce user efforts of text input during search.

In some embodiments, the search engines further include recommendationengines in addition to the search functionality. The search engines aremeant for search and recommendation of content items where the contentitems are meant for use on the input- and display-constrained devicesdescribed above. Example content items include, but are not limited to,the following: contacts, device applications, music items, web siteaddresses or uniform resource locators (URLs) for web browsing, businesslistings, and/or ring tones. In some embodiments, the search enginesreside on the device 106 or in the network 108.

As described in further detail below, the present system sends userinformation to disparate search engines along with a search query forprocessing by the search engines. User information includes contextinformation describing the environment in which the query is being sent,as well as profile information describing the user from a “usersignature.” A user signature is a data structure representing the user'suse of the device. The user signature includes information reflectingthe user's preferences and activities, such as temporal andlocation-based components, including a timestamp of the user's search,and/or the user's location. Techniques for generating a user signaturebased on user preferences, activities, and behavior include, but are notlimited to, those disclosed in U.S. Pat. No. 7,949,627, entitled Methodsand Systems for Selecting and Presenting Content based on LearnedPeriodicity of User Content Selection, filed Jul. 26, 2010, which isincorporated by reference herein.

Optionally, a recommendation query includes a search query or a browsequery. A search query refers to a query that is input by the user toinitiate a search. In some embodiments, the search query comprises textinput. In further embodiments, the search query text is generated orderived from non-textual visual or aural input. For example, a deviceaccepts speech input and converts the spoken words into text.Optionally, the present system uses text parsing such as part-of-speechtagging during the conversion. Alternatively, a device uses a cameramodule to capture a photograph, image, or barcode for conversion intotext for use in a search query. A browse query refers to a queryinitiated when the user does not actively enter a query but insteadperforms an action in a device application, such as activating a buttonor clicking a web page link. Optionally, a recommendation query includesa request to recommend results from a subset of search engines for aparticular place or time for the user, or a request to recommend afurther qualified subset of data items.

In some embodiments, for efficiency the present system avoids sendingcertain queries to an appropriate subset of vertical search engines inthe federation. For example, if a user does not habitually make use ofcontent from particular dataspaces or vertical search engines, thepresent system chooses those dataspaces or vertical search engines toskip or delay sending search requests. The present system generates anordering of search results for presentation to the user based on theuser information available to the federated search system.

FIG. 1 illustrates a system for personalized federated search wheresearch is done on a network system and results are returned to a clientdevice in accordance with some embodiments. FIG. 1 includes a serverfarm 102, a network 104, a personal device 106, and a computer 108. Aserver farm 102 can serve as a source of search results andrecommendation results with a network 104 functioning as a distributionframework. In some embodiments, the distribution framework is acombination of wired and wireless connections. Examples of possiblenetworks include cable television networks, satellite televisionnetworks, IP-based networks (including IP television networks), wirelessCDMA, GSM, GPRS, UMTS, and LTE networks. Optionally, the search deviceshave a wide range of interface capabilities, such as a personal device106 (e.g., a phone or Personal Digital Assistant (PDA)) with a limiteddisplay size and/or a reduced keypad with overloaded keys, a television108 a coupled with a remote control device 108 b having an overloadedkeypad, and a personal computer (PC) 110 with a reduced keyboard and acomputer display. According to another embodiment of the present system,the search happens locally on any of the devices 106, 108, 110, andsearch results are rendered locally on the user interface.

FIG. 2 illustrates a system for personalized federated search inaccordance with some embodiments. FIG. 2 includes a network side 224, apersonal device side 222, a runtime application user interface layer202, an activity tracking system 204, a smart learning system 206,groups 208 a, 208 b of vertical search engines, runtime resultscombiners 210 a, 210 b, interfaces 212 a, 212 b, 212 c, apersonalization profile & signature database 214, a SmartTag-enhanceddatabase 216, a SmartTagging interface 218, and SmartTagging systems220. The user is able to search for on-device personal content as wellas network-based content from one or more user interfaces, such as userinterfaces running on runtime application user interface (UI) layer 202.The user receives recommendations for on-device personal content andnetwork-based content from one or more user interfaces, including a userinterface running on runtime application UI layer 202. As describedabove, in some embodiments, a search engine includes a recommendationengine having the ability to make user recommendations.

The activity tracking system 204 tracks and processes informationprovided from the runtime application UI 202, including the user'ssearch, recommendation, and follow-up actions regarding on-devicepersonal content and/or network content. The activity tracking system204 and the smart learning system 206 use machine learning algorithms toprocess user behavior, profile, and activity information such as theuser's activities, smart tags, and enhanced metacontent pertaining tothe content he searched and acted on, and distill this user behavior,profile, and activity information into a user signature which capturesthe user's preferences and interests with regard to various distinctdataspaces, specific smart tags, various genres, and categories. Adataspace represents the subject matter of a particular search engine.In one implementation, the user signature includes one or more vectorsof numbers or weights for each of the above. The user signature furtherincludes temporal components to capture time-of-day- andday-of-week-dependent preferences and interests, as well as spatialcomponents to capture location- or place-dependent preferences andinterests.

The search queries and recommendations are served by groups 208 a, 208 bof independent vertical search engines. In some embodiments, the groups208 a, 208 b of vertical search engines include first-party searchengines, which are implemented as part of the present system, andthird-party search engines, which are implemented independently andintegrated into the present system. Group 208 a of vertical searchengines includes one or more search engines on the device side 222, andgroup 208 b of vertical search engines includes one or more searchengines in the network side 224. Optionally, the term vertical alsorefers to a single search engine which responds to search orrecommendation requests for multiple dataspace subject areas.

The present system further includes runtime results combiner (RRC)modules 210 a, 210 b. The RRCs impose an ordering on search results andauxiliary information from vertical search engines. To generate thisordering, each RRC 210 a, 210 b starts by conveying the queryinformation to the groups 208 a, 208 b of search engines, or to otherRRCs. The query information includes: (1) a query such as the search andrecommendation query, and (2) user information including (a) contextsuch as the time and location of the search request, and (b) a usersignature. For example, device-based RRC 210 a can use interface 212 ato convey the search query and the user information to group 208 a ofvertical search engines, and network-based RRC 210 b can use interface212 b to convey the search query and the user information to group 208 bof vertical search engines. Device-based RRC 210 a can also useinterface 212 c to send the search query and the user information tonetwork-based RRC 210 b. RRCs 210 a, 210 b also convey user informationto individual search engines in groups 208 a, 208 b, and gatherresponses using interfaces 212 a, 212 b in the form of search resultsand auxiliary information. Optionally, network-based RRC 210 b ordersthe search results based on user information and other informationfields in the search results, and then returns the ordered list ofresults back to the device-based RRC 210 a using interface 212 c.

In some embodiments, the present system uses a single RRC to generatepersonalized federated search results. The single RRC runs either on thenetwork side 224 or on the device side 222. If the single RRC runs onthe network side 224, then the modules running on the device side 222receive search results and ordering processed by modules running oncomputers separate from the device side 222, and the interfaces 212 a,212 b, 212 c connect the device side 222 to external computers runningthe respective modules. In some embodiments, at least one of the modulesdepicted on the device side 222, including at least one of the activitytracking system 204, the smart learning system 206, the group 208 a ofdevice-based search engines, the personalize profile and signaturedatabase 214, and the SmartTagged enhanced databases 216, runs externalto the device side 222, for example on the network side 224. If thesingle RRC runs on the device side 222, then the modules on the deviceside 222 actively process the search results returned from the groups208 a, 208 b of device-based vertical search engines in accordance withthe present system and method.

In further embodiments, an RRC is implemented as a tree data structure,in which internal tree nodes represent intermediate RRCs, and leaf treenodes represent vertical search engines. For example, device-based RRC210 a is understood to be the root of an RRC tree, with group 208 a ofdevice-based vertical search engines connected to device-based RRC 210 ausing interface 212 a, network-based RRC 210 b connected to device-basedRRC 210 a using interface 212 c, and group 208 b of network-basedvertical search engines connected to network-based RRC 210 b usinginterface 212 b. In such an RRC tree, group 208 b of network-basedvertical search engines represents leaf nodes of the tree rooted atdevice-based RRC 210 a. Network-based RRC 210 b represents an internaltree node, with group 208 b of network-based vertical search enginesconnected as leaf nodes of the tree to network-based RRC 210 b. Anembodiment having a single RRC interacting with vertical search enginesand an embodiment similar to FIG. 2 having a device-based RRC 210 a anda network-based RRC 210 b both represent instances of the RRC treedescribed above. For a single RRC, the RRC tree's root node is thesingle RRC and the vertical search engines are the leaf tree nodes. Forthe device-based RRC 210 a and the network-based RRC 210 b, the RRCtree's root node is the desktop-based RRC 210 a and the network-basedRRC 210 b is a single internal tree node.

Interfaces 212 a, 212 b, 212 c (1) carry query information includingquery information, user signature, and context, referred to as “incominginformation,” toward the vertical search engines, and (2) carry backresponses from the vertical search engines representing search resultsalong with auxiliary information referred to as “search signalinginformation elements” for each response. The RRCs 210 a, 210 b use thesearch signaling information elements (shown in FIG. 3) to inform thecombining and ordering of the search results for presentation to theuser.

The present system uses the SmartTagged enhanced databases 216, theSmartTagging systems 220 serving search systems, and the SmartLearninginterface 218 to track and maintain a set of entities known asSmartTags. Example SmartTags include people, bands, artists, actors,movies, television shows, important content categories, and/or onlineencyclopedia pages and topics. Optionally, the SmartTags have metadataassociated with the tagged content, which expands the number ofpotential search queries that may be matched by a particular searchquery. Techniques for associating content with SmartTags include, butare not limited to, those disclosed in U.S. patent application Ser. No.12/879,141, filed Sep. 10, 2010, entitled Method of and System forPresenting Enriched Video Viewing Analytics, which is incorporated byreference herein. In some embodiments, the SmartTags provide a way tocombine and order the search results by grouping or aggregating searchresults associated with related SmartTags. In further embodiments, thefederated search results are presented as dynamic categories or folders.For example, an incremental search for “lad” returns a dynamic folderabout the performer Lady Gaga containing search results federated from amusic vertical search engine, a ring tone vertical search engine, and amusic video search engine. The SmartTagging systems 220 gather contentautomatically for SmartTags by crawling content sites and deduplicatingentities which appear on multiple content sites. The SmartTaggingsystems 220 send these entities over the SmartLearning interface 218 tothe SmartTagged enhanced databases 216. In some embodiments, theSmartTagged enhanced databases 216 support rules for matching relatedmetacontent with SmartTags. For example, if a music track search resultfrom a music vertical search engine contains metacontent descriptorfields mentioning performers as “Lennon, McCartney,” rules in theSmartTagged enhanced databases 216 support the present system toassociate the metacontent with a SmartTag for the music band TheBeatles. Similarly, if a baseball video clip search result from a videovertical search engine contains metacontent descriptor fields mentioning“NYY@BOS,” rules in the SmartTagged enhanced databases 216 support thepresent system to associate the metacontent with SmartTags for thebaseball teams the Boston Red Sox and for the New York Yankees. TheSmartTagged enhanced databases 216 store a unique identifier referred toas the SmartTag ID for all entities in the database. Accordingly, insome embodiments, the user information such as the user signatureincludes preferred SmartTags or SmartTag IDs associated with the user.Although the SmartTagged enhanced databases 216 are depicted on thedevice side 222, in some embodiments, the databases are external to thedevice side 222, and the SmartTags are transmitted to thepersonalization profile & signature database 214 over an interface.

FIG. 3 illustrates a network view of a system for personalized federatedsearch in accordance with some embodiments. FIG. 3 includes theinterface 212 c; the device side 222; the network side 244; incominginformation 304; a host 306 having a network-based runtime resultscombiner 210 b and a dataspace-specific configuration informationcombiner database 318; a host 308 having a music vertical search serverand a content database 318; a host 310 having an applications verticalsearch server and a content database 320; a host 312 having a moviesvertical search server, a ring tones vertical search server, and contentdatabases 322 a, 322 b; and a host 314 a having a business listingsfrontend to hosts 314 b, 314 c having business listings servers andcontent databases 324 a, 324 b. An interface 212 c carries incominginformation 304 from a device side 222 to a network-based runtimeresults combiner (RRC) 210 b running on a host 306. The host 306 isconnected to a group 208 b of several independent vertical searchengines. Example vertical search engines include a music search serverrunning on host 308, a device applications search server running on host310, a movies search server and a ring tone search server, both runningon host 312, and a business listings search server running on host 314a, which aggregates search results from business listings search serversrunning on hosts 314 b-314 c. Although FIG. 3 illustrates an exampleembodiment, the implementation details such as the vertical searchengines running on the hosts, the separation of the hosts, and thearchitecture of the present system may vary.

The hosts 306, 308, 310, 312, 314 a-314 c are connected by an interface316. In some embodiments, the interface 316 is a Local Area Network(LAN), Wide Area Network (WAN), or other network topology. In someembodiments, the network-based RRC 210 b communicates with the verticalsearch engines via messages using the Transmission ControlProtocol/Internet Protocol (TCP/IP) internet protocol suite on theinterface 316. Each vertical search engine can be independent of theother vertical search engines and independent of the network-based RRC210 b. In some embodiments, each vertical search engine itself includesmultiple computing processes distributed across multiple hosts, as shownwith the business listings search server running on hosts 314 a-314 c.Optionally, each vertical search engine shares a host with one or moreother search servers, as shown with the movies search server and theringtones search server which are both running on host 312. Thenetwork-based RRC 210 b combines and orders search results and auxiliaryinformation, and sends the search results and auxiliary information overthe interface 212 c for use by modules on the device side 222.

To combine and order search results and auxiliary information frommultiple vertical search engines for presentation to the user, thenetwork-based RRC 210 b accesses and stores information relating to eachfield of the search results returned by the vertical search engines. Thesearch results contain multiple fields, representing informationprovided by content databases having fields to store (1) content and (2)metadata relevant to the content, such as the content's title, author,and/or creation and last modification date. For example, the musicsearch server has a content database 318, the applications search serverhas a content database 320, the movies search server has a contentdatabase 322 a, the ringtones search server has a content database 322b, the first business listings server has a content database 324 a, andthe last business listings server has a content database 324 b.

These content databases use fields of the databases to store (1)substantive content relevant to the user's search query, and (2)metadata related to the substantive content. Certain fields pertain tothe substantive content, and certain fields pertain to the metadata. Themetadata is also referred to as information elements. The network-basedRRC 210 b stores these information elements in a database 318 called thedataspace-specific configuration information database. In someimplementations, the dataspace-specific configuration informationdatabase 318 resides on the same host 306 as the network-based RRC 210b, or on a different host connected to the network-based RRC 210 b viaan interface.

As described above, the RRCs (1) send query information to the verticalsearch engines, referred to as “incoming information,” and (2) receiveresponses from the vertical search engines including search results andauxiliary information, referred to as “search signaling informationelements” 328. The communication protocols for sending and receivingthis information use interfaces 212 a-212 c (shown in FIG. 2). Thesearch signaling information elements 328 represent selection criteriafrom the user that cause a given result to be sent by any of thevertical search engines.

In some embodiments, the incoming information carried on interfaces 212a-212 c (shown in FIG. 2) towards the vertical search engines includesone or more of the following: text query if the user enters a searchquery, source of the text query if the present system converted analternative input, SmartTag ID if the user enters a browse query, userlocation, user date and time, and user signature or part thereof. Asdescribed above, in some embodiments, the present system convertsalternative inputs such as images, barcodes, or spoken queries intosearch queries. Optionally, the present system includes the source ofthese alternative inputs as information sent to the vertical searchengines. As described above, a browse query refers to a query initiatedwhen the user performs an action in a device application, such asactivating a button or clicking a web page link. Also as describedabove, a SmartTag refers to an entity having a name and content, whichis associated with metadata for searching and grouping. As describedabove, a SmartTag ID refers to a unique identifier for a SmartTag, andall entities in the SmartTagged enhanced database 216 have a SmartTag IDunique identifier. In some embodiments, the device 106 reports theuser's physical location in the form of latitude and longitudecoordinates, cellular ID, or using a Global Positioning Satellite (GPS)module. The date and time information for the user includes the time,day, and date for the user. The user signature includes the informationdescribed earlier regarding the user's profile, activity, behavior, orusage habits. In some implementations, the user signature includescorrelations or probabilities of alternative inputs, such as speech,barcodes, or images, being used for particular dataspaces or verticalsearch engines. For example, the present system correlates that the userconsistently prefers search results from a phone directory verticalsearch engine, when a search query is generated from speech input into amobile device. Optionally, the incoming information carries only part ofthe user signature, for example, the part relevant to the verticalsearch engine's dataspace for use in forming search results.

Information returned from the groups 208 a, 208 b of vertical searchengines to the RRCs 210 a, 210 b on interfaces 212 a-212 c (shown inFIG. 2) includes (a) ordered search result lists from each search engineand (b) auxiliary information. Auxiliary information pertaining to eachsearch result includes display and/or action-link information for theresult. Example auxiliary information includes display strings, links tojoin on-device or network actions, highlight information to emphasizethe portions or passages of a search result that matched, searchsignaling information elements 328, dataspace- orvertical-search-engine-specific configuration information, and otherdisplay information for the present system to use in its display to theuser.

In some embodiments, the search signaling information elements 328associated with a search result includes metadata or informationdescribing and specifying attributes of the result that led to its beingchosen as a result by the vertical search engine. Example searchsignaling information elements 328 include the following: the dataspaceor vertical search engine that provided the search result; a relevancevector; a textual location vector; a disjunction indicator vector; aspelling correction vector; a complete word match vector; a field matchvector; a location proximity indicator; and/or a location query-placeindicator.

A relevance vector refers to a vector or collection listing multipledimensions and subattributes with a relevance score for each dimensionor subattribute. In some implementations, for each dimension orsubattribute, the vertical search engine provides an attribute value forthe search result, along with the maximum possible value of theattribute for any possible search result from the vertical searchengine. Example attributes include a query match score of how closelythe query text matches the result content; a result popularity measuringa popularity of the result in the general societal culture; personalizedrelevance measuring a result's relevance to the user himself or herself,based on the user signature provided by the RRC 210 a, 210 b; and/ortemporal or spatial relevance scores measuring whether a search resulttends to be relevant at the current time and place, which additionallydepends on information provided from the user signature. As an exampleof temporal relevance, a vertical search engine provides a temporalrelevance score reflecting that children's movies are more likely to beplayed on weekends, or adult movies are more likely to be played atnight. Based on the time that the user search query is made, thetemporal relevance score changes.

A textual location vector refers to a vector or collection containingone element per search query word which indicates the search queryword's location in metacontent fields provided by the search result.Each element contains textual locations pertaining to a correspondingsearch query word's match with the search result metacontent.Optionally, each element of the vector further contains a metacontent-idfor differentiating among multiple metacontent fields for the samesearch result, a field-id within the metacontent, and/or the location ofthe matching word within the field's word sequence. For example, anarticle has a title field of “Let's meet Tom Cruise” and a descriptionfield containing the content of the article. The title field is storedin a content database as field 1 and the description field is stored inthe content database as field 5. In some implementations, in response toa search query for “tom cruise,” a textual location vector correspondingto a search result has the form ((1,3), (5,27)). For the first element,the tuple (1,3) indicates that the search result contains a match forthe search query in field 1, the title field, starting at location 3.For the second element, the tuple (5,27) indicates that the searchresult contains a match for the search query in field 5, the descriptionfield, starting at location 27.

A disjunction indicator vector refers to a vector or collectioncontaining one element for each query word along with a boolean orbinary bit indicating whether the corresponding query word matched theresult. In some implementations, the disjunction indicator vector isused when a search query includes a disjunctive operator such as OR, asshown in an example search query for “tom OR cruise.” If a search resultcontains the word “tom” but not “cruise,” a corresponding disjunctionindicator vector might be (1,0). If a search result contains the word“cruise” but not “tom,” a corresponding disjunction indicator vectormight be (0,1). If a search result contains “tom” and “cruise,” acorresponding disjunction indicator vector might be (1,1).

A spelling correction vector refers to a vector or collection containingone element for each search query word which indicates if thecorresponding search query word exactly matched a substring of thesearch result's metacontent. Optionally, if the match was a spellingcorrection match, the vector contains an indicator of the spellingcorrection “distance” between the query word and the matched prefix,representing how “far” the corrected search query word was from thematched prefix.

A complete word match vector refers to a vector or collection containingone element for each query word indicating whether the correspondingsearch query word exactly matched a full word of the result'smetacontent. If the match was a prefix or a substring match, theindicator's value is “false” or “0.”

A field match vector refers to a vector or collection containing oneelement for each field of each metacontent descriptor of the searchresult. Each element indicates whether some or all of the words of thesearch query constitute a match of all words of the search resultmetacontent descriptor field. If it is a match of the entire field, theelement indicates whether it is an exact match or a spelling-correctedmatch. In case of a spelling-corrected match of the entire field, theelement further indicates the spelling correction distance for theentire field, as described above. If it is not a match of the entirefield, the element indicates the extent of the match. Example matchextent indicators include a number of matched words or matchedcharacters, and/or a total number of words and characters in the field.The element further indicates whether the match is an exact match or aspelling-corrected match of any part of the field, and if it is aspelling corrected match, the element indicates the spelling correctiondistance, as described above.

A location proximity indicator refers to a value indicating whether thesearch result is close to a specified location from the user. In someembodiments, the location proximity indicator further includes adistance estimate from the user's specified location to the searchresult. In some embodiments, the specified location represents alocation of the device 106 in the form of latitude and longitudecoordinates, cellular ID, or using a Global Positioning Satellite (GPS)module. In some embodiments, the specified location is input by the userin the search query, or by the present system in the browse query orrecommendation query.

A location query-place indicator refers to a value indicating whether apart of the search query matches an entity recognized by the system tobe a place. The location query-place indicator indicates whether thesearch result is close to or in the matched place, or includes adistance estimate from the matched place.

Advantageously, the auxiliary information, including the searchsignaling information elements 328 described above, allows the presentsystem to return relevant search results even with partial prefixmatches in general, wherein the partial prefixes are of the kindaccepted as input by a user on an input- or display-constrained deviceusing incremental search. Many of the problems addressed by the presenttechniques are faced in incremental search and recommendation systems.

In some embodiments, in addition to the search signaling informationelements 328 described above which are specific to a search responsereceived by the RRCs 210 a, 210 b, the RRCs 210 a, 210 b are furtherprovisioned with configuration information specific to dataspaces orvertical search engines which applies to all search results receivedfrom a particular vertical search engine. The RRCs 210 a, 210 b use thedataspace- or vertical-search-engine-specific configuration informationto order search results. The dataspace- orvertical-search-engine-specific configuration information capturesspecific aspects of the vertical search engines being searched. Exampleaspects of the vertical search engines include the following: importancerelative to other vertical search engines of reordering criteria ofdifferent metacontent descriptor fields, importance relative to othervertical search engines of spelling correction matches, importancerelative to other vertical search engines of disjunction indicators,relative distance compared to other vertical search engines for locationindicators, whether any match at all for a vertical search engine isgood enough to be deemed important, and/or the relative importancecompared to other vertical search engines of complete word matches.Dataspace- or vertical-search-engine-specific configurations depend onthe quality of the source of the metacontent of the listings for thatvertical.

In some embodiments, the importance with regard to reordering criteriaof different fields in the metacontent differs between vertical searchengines. For example, in case of listings from a telephone directoryvertical search engine, certain fields such as retail chain name andcertain custom-created fields in the SmartTag database are deemed by theRRCs 210 a, 210 b to be as important as the title of the listing. TheRRCs 210 a, 210 b treat a match on a retail chain field and a match on acustom field for a search result from a telephone directory verticalsearch engine as equally important. A match in an address field is notas important compared to a match in the retail chain field. Similarly amatch on a title field for an online encyclopedia vertical search engineis deemed more important than a match of a generic textual keyword in acontent field of the encyclopedia article search result. For songsrepresenting search results from a music vertical search engine, thepresent system deems the artist name to be as important as the songname.

In some embodiments, the importance of spelling correction matchesdiffers between vertical search engines or between search result fields.For example, in an Indian music vertical search engine, even if amatched song search result is named “Main Zindagi Ka Saath” based on auser's search query of “Mein Jindagi Kaa Saath” the present system deemsthis search result almost akin to an exact match. However if a user'squery is “Andy's Restaurant” but a matched search result candidate is“Arby's Restaurant,” the present system continues to treat this searchresult as not an exact match but a spelling correction match, which ispresented lower in an ordering of search results than an exact match.

In some embodiments, the importance of disjunction indicators differsbetween vertical search engines or between search result fields. Forexample, a user's search query is “If this is it,” whereas an intendedHuey Lewis song search result is listed as “This is it.” The presentsystem indicates to the RRCs 210 a, 210 b to treat such a disjunctionindictor as an exact match, even if a disjunction indicator vector, asdescribed above, tracks that the first term in the search query, “if,”did not match a word in the search result.

In some embodiments, a location proximity indicator and distance differsbetween vertical search engines. For a business listings result from atelephone directory vertical search engine, the present system deems asearch result having a distance of about one mile to be “nearby,” aboutten miles as “close,” and about ten to about thirty miles as “local.”For search result describing tourist destination search results from aPlaces of Tourist interest vertical search engine, the present systemdeems a search result having a location of up to about forty miles as“nearby,” about forty to about one hundred miles as “close,” and aboutone hundred to about one hundred fifty miles as “local.”

In some embodiments, the present system deems any match as good enoughfor that match to be deemed important for certain vertical searchengines. For example, for a current movies vertical search engine, giventhe “perishability” of search result information pertaining movies intheaters, the present system deems even a match on a movie crew searchresult field as good enough to present the movie search resultrelatively higher in ordering the search results for display to theuser. Similarly, given the perishability of search results relating to“hot news” such as flight status information or search results relatingto New York Stock Exchange (NYSE) stock information, such matches aretreated by the RRCs 210 a, 210 b as important and are ordered so thatthey trump most other search results when displayed to the user.

Lastly, in some embodiments, the relative importance of complete wordmatches compared to partial word matches differs across dataspaces.Results having more complete word matches from one dataspace areprioritized over results having more partial word matches from otherdataspaces.

As described above, the RRCs 210 a, 210 b order search results returnedfor the search queries, browse queries, and recommendation queries usedin federated search, personalized federated search, and personalizedrecommendations. The RRCs 210 a, 210 b perform this ordering using theauxiliary information, such as the search signaling information 328 andthe dataspace- or vertical-search-engine-specific configurationinformation, individually or in combination with the incominginformation. Examples of computing an ordering of search results includethe following: suppressing or deprioritizing search results fromdataspaces or vertical search engines in which the user signatureindicates that the user has never shown interest; suppressing ordeprioritizing search results from dataspaces or vertical search enginesin which two-word search queries match two different metacontentdescriptor fields compared to search queries which match a singlemetacontent descriptor field; promoting or deprioritizing search resultsbased on the location proximity indicator search signaling informationelement, used alone or in combination with the dataspace-specificinformation about relative distance for location indicators; promotingor deprioritizing search results based on whether a search resultrepresents a disjunctive match or a spelling-corrected match, used aloneor in combination with the dataspace-specific information regarding theimportance of disjunction indicators or the importance ofspelling-correction matches; and/or prioritizing search results having atemporal or location-based component, in combination with the incominginformation or user signature providing the user's current time orcurrent location.

In some embodiments, the present system suppresses or deprioritizessearch results for which two-word search queries match two differentfields of the metacontent descriptor fields, compared to search querieswhich match a single field of the metacontent descriptor fields, forvertical search engines or dataspaces representing content that wouldbenefit from such ordering. The RRCs 210 a, 210 b compute whether asearch result has one or two fields involved in a search match and theidentity of the fields involved from the textual location vectordescribed above. The RRCs 210 a, 210 b further base the decision ofwhether to deprioritize a search result based on the identity of themetacontent descriptor fields using information from the dataspace- orvertical-search-engine-specific configuration information, includinginformation regarding the importance of reordering criteria of differentmetacontent descriptor fields, described above.

In some embodiments, the present system promotes or deprioritizes searchresults based on the location proximity indicator, used alone or incombination with the dataspace-specific information about relativedistance for location indicators. The RRCs 210 a, 210 b use the searchsignaling information element representing a location proximityindicator to indicate the proximity of a particular search result,including the distance between the search result and the user's locationas determined from the incoming information sent to the vertical searchengines. Furthermore, the RRCs 210 a, 210 b use the relative distancefor location indicators dataspace-specific information, which indicatesfor a search result from a particular dataspace, how the RRCs 210 a, 210b could interpret or evaluate the distance of the search result in thecontext of the dataspace-specific configuration information.

The elements of the systems set forth herein are illustrative only andmodifications are within the scope of the invention. Certain elementsdescribed as residing on the device can be located on the network sideinstead. For example, the runtime application UI layer 202 can reportuser activities to an activity tracking system 204 located on thenetwork side, which could be coupled with a smart learning system 206 onthe network side that could work in conjunction with similar elementspresent on the device side of the system (shown in FIG. 2).

FIGS. 4-7 illustrate screenshots of the present system in accordancewith some embodiments. Although the screenshots illustrated in thepresent disclosure are a small set, the number of uses for the presentsystem is large. FIG. 4 illustrates a screenshot of an example searchresponse for a user's search query in accordance with some embodiments.FIG. 4 includes a search query 402, and search results 404 a, 404 b, 404c, and 404 d. A user inputs a search query 402 for “john.” The searchresults 404 a-404 d are federated from multiple vertical search engines.For example, search results 404 a-404 c are presented from a businesslistings vertical search engine. Search result 404 d is returned from anonline encyclopedia vertical search engine. The user has a usersignature which indicates that the user's search use is skewed towardpreferring business listing search results which are located very closeto the user. Accordingly, the ordering and presentation of searchresults 404 a-404 c from the business listings vertical search engineshow that search results representing businesses titled “john” locatedwithin a mile of the device 106 (shown in FIG. 1) are presented mosthighly in the user's top search results, compared to search result 404 dfrom the online encyclopedia vertical search engine which is presentedlower in the ordering of search results.

FIG. 5 illustrates a screenshot of an example search response for a userhaving a different user signature in accordance with some embodiments.FIG. 5 includes a search query 402, and search results 504 a, 504 b, 504c, and 504 d. As illustrated in FIG. 4, a user inputs a search query 402for “john.” The personalized search results are federated from multiplevertical search engines. The personalized search results 504 a, 504 dreturned by a business listings vertical search engine include a searchresult 504 a to a relatively well-known real estate chain and a searchresult 504 d for a business listing that is close-by in proximity to theuser. The search results 504 b, 504 c from an online encyclopediavertical search engine describe famous individuals such as John Lennonand John the Baptist. The user has a user signature which indicates amore balanced search profile not as dependent on search resultsrepresenting business listings which are located very close to the user.As before, the search results 504 a-504 d are federated from multiplesearch engines, but the present system and method customize the orderingand presentation to the user based on the user signature.

FIG. 6 illustrates a screenshot of an example personalized searchresponse for a user's incremental search in accordance with someembodiments. FIG. 6 includes a search query 602, and search results 604a, 604 b, 604 c, and 604 d. A user enters a partial and incompletesearch query 602 for “dunk.” The search results 604 a-604 d are allreturned by a business listings vertical search engine. The user has auser signature indicating that he frequently acts upon search resultsfor DUNKIN' DONUTS® doughnut shops across all the places that the uservisits. Accordingly, the present system and method orders search results604 a-604 d for DUNKIN' DONUTS® business listings highly in thepresentation of the user's top search results. Furthermore, based on theuser signature, the present system and method orders the search results604 a-604 d according to their distance from the user's device. Searchresult 604 a is listed as “<1 mi[le]” from the user's device, searchresults 604 b, 604 c are listed as “1 mi[le]” from the user's device,and search result 604 d is listed as “2 mi[les]” from the user's device.

FIG. 7 illustrates a screenshot of an example personalized searchresponse for a user's incremental search having a different usersignature in accordance with some embodiments. FIG. 7 includes a searchquery 602, and search results 704 a, 704 b, 704 c, 704 d, and 704 e. Asillustrated in FIG. 6, a user enters a partial and incomplete searchquery 602 for “dunk.” The search results are federated from multiplevertical search engines. Search results 704 a, 704 d, 704 e are from anonline encyclopedia vertical search engine. Search results 704 b, 704 care from a business listings vertical search engine. The user has a usersignature indicating a more balanced search profile not skewed towardsacting on search results for DUNKIN' DONUTS® doughnut shops.Accordingly, the present system presents search result 704 a including adictionary listing of the meaning of the word dunk, followed by searchresults 704 b, 704 c indicating business listings of DUNKIN' DONUTS®doughnut shops which are in close proximity to the user, and followed bysearch results 704 d, 704 e to online encyclopedia articles havingtitles of Dunkirk and Slam dunk.

FIG. 8 illustrates a diagram that depicts the various components of auser device, in accordance with some embodiments. FIG. 8 includes adisplay 801, a processor 802, a volatile memory store 803, a keyboard804, a remote connectivity module 805, and a persistent memory store806. The user device communicates with a user via a display 801 and akeyboard 804. Optionally, this keyboard 804 is an overloaded keyboardthat produces ambiguous text input. Computation is performed using aprocessor 802 that stores temporary information in a volatile memorystore 803 and persistent data in a persistent memory store 806. In someembodiments, either or both of these memory stores hold the computerinstructions for the processor to perform the logic described above. Thedevice is operable to connect to a remote system using a remoteconnectivity module 805.

Using the present techniques, embodiments of the invention enable itemsfrom difference search spaces to be presented in the same result set ina manner most helpful to the individual user. Implementations of thepresent system and method enable the searching of multiple searchengines incorporating information about a user and information about thesearch engines in a way that presents the information most relevant tothe search query in an easily accessible manner. As mentioned above, insome embodiments, these aspects are particularly helpful whenimplemented on input- and display-constrained devices, on whichinputting search queries and scanning through poorly ordered searchresults is particularly burdensome.

In some embodiments, the techniques and systems disclosed herein areimplemented as a computer program product for use with a computersystem. Such implementations include a series of computer instructions,or logic, fixed either on a tangible medium, such as a computer readablemedium (e.g., a diskette, CD-ROM, ROM, flash memory or fixed disk) ortransmittable to a computer system, via a modem or other interfacedevice, such as a communications adapter connected to a network over amedium.

In some embodiments, the medium is either a tangible medium (e.g.,optical or analog communications lines) or a medium implemented withwireless techniques (e.g., microwave, infrared or other transmissiontechniques). The series of computer instructions embodies all or part ofthe functionality described herein with respect to the system. Thoseskilled in the art should appreciate that such computer instructions canbe written in a number of programming languages for use with manycomputer architectures or operating systems.

Furthermore, optionally such instructions are stored in any memorydevice, such as semiconductor, magnetic, optical or other memorydevices, and are transmitted using any communications technology, suchas optical, infrared, microwave, or other transmission technologies.

In some embodiments, it is expected that such a computer program productis distributed as a removable medium with accompanying printed orelectronic documentation (e.g., shrink wrapped software), preloaded witha computer system (e.g., on system read only memory (ROM) or fixeddisk), or distributed from a server or electronic bulletin board overthe network (e.g., the Internet or World Wide Web). Of course,optionally some embodiments are implemented as a combination of bothsoftware (e.g., a computer program product) and hardware. Still otherembodiments of the invention are implemented as entirely hardware, orentirely software (e.g., a computer program product).

To summarize, embodiments of the present invention provide for a userinterface that enables the user to search multiple search engines whileincorporating information about the user and information about thesearch engines into the searching, ordering, and presentation of thesearch results.

It will be appreciated that the scope of the present invention is notlimited to the above-described embodiments, but rather is defined by theappended claims; and that these claims will encompass modifications ofand improvements to what has been described.

1. A computer-implemented user interface method of submitting searchrequests to at least two search engines and presenting search resultstherefrom customized using content preferences learned about a user, themethod comprising: sending query information to the at least two searchengines, the query information including (a) a query identifying desiredcontent, and (b) user information, the user information including (i)context information describing the environment in which the queryinformation is being sent, the context information adapted into a syntaxunderstandable by at least one of the at least two search engines, and(ii) a user signature representing content preferences learned about theuser; receiving at least one set of a search result and auxiliaryinformation from the at least one of the at least two search engines inresponse to sending the query information, the auxiliary information ofthe set including information describing attributes of the search resultof the set that led to the search result being chosen by the at leastone of the at least two search engines; ordering the at least one searchresult based at least in part on the auxiliary information; andpresenting the ordered search results to the user.
 2. The method ofclaim 1, wherein the auxiliary information includes at least one of: anidentification of a dataspace associated with the corresponding searchresult, an identification of a search engine that provided the searchresult, a relevance list, wherein a relevance list element represents aset of an attribute of the search result and a value indicating therelevance of the attribute to at least one of the query and the userinformation, a textual location list, wherein a textual location listelement represents the location of search terms from the queryinformation in fields provided by the search result, a disjunctionindicator list, wherein a disjunction indicator list element representswhether a corresponding search term from the query information matchedthe search result, a spelling correction list, wherein a spellingcorrection list element represents whether a corresponding search termfrom the query information exactly matched a substring of fields fromthe search result, and wherein the spelling correction list elementrepresents a value indicating the distance between the correspondingsearch term and the matched substring, if a match was based on anapplied spelling correction, a complete word match list, wherein acomplete word match list element represents whether a correspondingsearch term from the query information exactly matched a full word offields from the search result based on an applied spelling correction, afield match list, wherein a field match list element represents whetherthe search query matched all words of a field of the search result, alocation proximity indicator representing a value for the distance fromthe search result to a specified location, and a location query-placeindicator representing whether a part of the search query matched anentity recognized to be a place having a location.
 3. The method ofclaim 1, wherein the ordering is further based on search engine generalinformation, the search engine general information describing the atleast one search engine.
 4. The method of claim 3, wherein the orderingbased on the search engine general information is based on a comparisonbetween the search engine general information and the user information.5. The method of claim 4, wherein the search engine general informationincludes at least one of a measure of importance relative to othersearch engines of reordering criteria of different metacontentdescriptor fields, a measure of importance relative to other searchengines of spelling correction matches, a measure of importance relativeto other search engines of disjunction indicators, relativeinterpretation of distance compared to other search engines for locationindicators, whether the mere existence of a search result from the atleast one search engine indicates that the search result should bepresented as important, and the relative importance compared to othersearch engines of complete word matches.
 6. The method of claim 1,further comprising selecting the search engines to which the queryinformation is sent based on a measure of the use by the user of thecorresponding search engines.
 7. The method of claim 1, wherein thequery identifying desired content includes at least one of a searchquery and a browse query, wherein the search query is initiated via atleast one of text input by the user and text derived from alternativeinput by the user, the alternative input including at least one ofspeech, images, and barcode scans, and wherein the browse query isinitiated via the user performing an action in a device applicationother than text input.
 8. The method of claim 1, wherein the contentpreferences learned about the user include at least one of a userprofile, user behavior, and user activity.
 9. A system for submittingsearch requests to at least two search engines and presenting searchresults therefrom customized using content preferences learned about auser, the system comprising: a computer memory store comprisinginstructions in computer readable form that when executed cause acomputer system to send query information to the at least two searchengines, the query information including (a) a query identifying desiredcontent, and (b) user information, the user information including (i)context information describing the environment in which the queryinformation is being sent, the context information adapted into a syntaxunderstandable by at least one of the at least two search engines, and(ii) a user signature representing content preferences learned about theuser; receive at least one set of a search result and auxiliaryinformation from the at least one of the at least two search engines inresponse to sending the query information, the auxiliary information ofthe set including information describing attributes of the search resultof the set that led to the search result being chosen by the at leastone of the at least two search engines; order the at least one searchresult based at least in part on the auxiliary information; and presentthe ordered search results to the user.
 10. The system of claim 9,wherein the auxiliary information includes at least one of: anidentification of a dataspace associated with the corresponding searchresult, an identification of a search engine that provided the searchresult, a relevance list, wherein a relevance list element represents aset of an attribute of the search result and a value indicating therelevance of the attribute to at least one of the query and the userinformation, a textual location list, wherein a textual location listelement represents the location of search terms from the queryinformation in fields provided by the search result, a disjunctionindicator list, wherein a disjunction indicator list element representswhether a corresponding search term from the query information matchedthe search result, a spelling correction list, wherein a spellingcorrection list element represents whether a corresponding search termfrom the query information exactly matched a substring of fields fromthe search result, and wherein the spelling correction list elementrepresents a value indicating the distance between the correspondingsearch term and the matched substring, if a match was based on anapplied spelling correction, a complete word match list, wherein acomplete word match list element represents whether a correspondingsearch term from the query information exactly matched a full word offields from the search result based on an applied spelling correction, afield match list, wherein a field match list element represents whetherthe search query matched all words of a field of the search result, alocation proximity indicator representing a value for the distance fromthe search result to a specified location, and a location query-placeindicator representing whether a part of the search query matched anentity recognized to be a place having a location.
 11. The system ofclaim 9, wherein the ordering is further based on search engine generalinformation, the search engine general information describing the atleast one search engine.
 12. The system of claim 11, wherein theordering based on the search engine general information is based on acomparison between the search engine general information and the userinformation.
 13. The system of claim 12, wherein the search enginegeneral information includes at least one of a measure of importancerelative to other search engines of reordering criteria of differentmetacontent descriptor fields, a measure of importance relative to othersearch engines of spelling correction matches, a measure of importancerelative to other search engines of disjunction indicators, relativeinterpretation of distance compared to other search engines for locationindicators, whether the mere existence of a search result from the atleast one search engine indicates that the search result should bepresented as important, and the relative importance compared to othersearch engines of complete word matches.
 14. The system of claim 9, thecomputer memory store further comprising instructions that cause thecomputer system to select the search engines to which the queryinformation is sent based on a measure of the use by the user of thecorresponding search engines.
 15. The system of claim 9, wherein thequery identifying desired content includes at least one of a searchquery and a browse query, wherein the search query is initiated via atleast one of text input by the user and text derived from alternativeinput by the user, the alternative input including at least one ofspeech, images, and barcode scans, and wherein the browse query isinitiated via the user performing an action in a device applicationother than text input.
 16. The system of claim 9, wherein the contentpreferences learned about the user include at least one of a userprofile, user behavior, and user activity.